






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   :      List[Any]				=		logging.get_logger(__name__)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   UniSpeechSatForSequenceClassification.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Tuple   =   downstream_dict['''projector.weight''']
				__magic_name__		:str   =   downstream_dict['''projector.bias''']
				__magic_name__		:Union[str, Any]   =   downstream_dict['''model.post_net.linear.weight''']
				__magic_name__		:Optional[int]   =   downstream_dict['''model.post_net.linear.bias''']
				return model

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Union[str, Any]   =   downstream_dict['''model.linear.weight''']
				__magic_name__		:List[Any]   =   downstream_dict['''model.linear.bias''']
				return model

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   UniSpeechSatForXVector.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Dict   =   downstream_dict['''connector.weight''']
				__magic_name__		:Optional[Any]   =   downstream_dict['''connector.bias''']
				for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
								__magic_name__		:Any   =   downstream_dict[
								    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								__magic_name__		:Any   =   downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

				__magic_name__		:Optional[int]   =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
				__magic_name__		:Dict   =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
				__magic_name__		:Tuple   =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
				__magic_name__		:Optional[Any]   =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
				__magic_name__		:Optional[int]   =   downstream_dict['''objective.W''']
				return model

@torch.no_grad()
def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   torch.load(snake_case,	map_location='''cpu'''			)

				__magic_name__		:Optional[Any]   =   checkpoint['''Downstream''']

				__magic_name__		:List[Any]   =   UniSpeechSatConfig.from_pretrained(snake_case			)
				__magic_name__		:Optional[int]   =   WavaVecaFeatureExtractor.from_pretrained(
				    snake_case,	return_attention_mask=snake_case,	do_normalize=snake_case			)

				__magic_name__		:Any   =   hf_config.architectures[0]
				if arch.endswith('''ForSequenceClassification'''			):
								__magic_name__		:Tuple   =   convert_classification(snake_case,	snake_case,	snake_case			)
				elif arch.endswith('''ForAudioFrameClassification'''			):
								__magic_name__		:int   =   convert_diarization(snake_case,	snake_case,	snake_case			)
				elif arch.endswith('''ForXVector'''			):
								__magic_name__		:Union[str, Any]   =   convert_xvector(snake_case,	snake_case,	snake_case			)
				else:
								raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''			)

				if hf_config.use_weighted_layer_sum:
								__magic_name__		:List[Any]   =   checkpoint['''Featurizer''']['''weights''']

				hf_feature_extractor.save_pretrained(snake_case			)
				hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Dict				=		argparse.ArgumentParser()
					parser.add_argument(
					    """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
					)
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
					parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





